What is the true cost of a buggy software? WorldPay, the renowned payment processing firm, that processes 36 million payments, suffered major glitches for three weeks due to software failure, while British Airways faced major IT failures in 2017, impacting their call centres, website and mobile apps and affecting around 70,000 passengers as flights were cancelled. All of this left experts predicting that BA could face a final bill of over £100million in compensation costs.
Yet, the true cost of a software failure is not restricted to loss of revenue. There are a significant reputation and competitive risk associated with poor quality software. In this age of rapid digital transformation, customer experience drives business strategy and technology. To meet the rising expectations of the digital customers, enterprises have embraced the agile and DevOps approach. Software quality and testing are critical to meet the release velocity, quality and stability required by customers’ digital experiences. Test and QA teams have adopted test automation to meet the mandate of speed, greater complexity, and error-free releases. Software test automation led the first wave of change that provided scale, efficiency and faster time-to-market for the QA process.
Continuous integration (and continuous testing) has shrunk the testing lifecycle further, adding to the efficiency gains. The agile mantra is ‘high quality at speed’. Yet, businesses still struggle with a sluggish test suite – slowed down by a mounting QA backlog, automating large volumes of test cases, poor visibility and inadequate coverage.
‘Programmes mirror human logic, but they don’t mirror intuitive thought’ – Rich Friedrich, director of systems software at Hewlett Packard Labs.
In other words, test automation needs to be smarter and more intuitive. It should learn from trend analyses of legacy data so that you can anticipate error-patterns and avoid re-inventing the wheel. This is where disruptive technology trends like BOTs, artificial intelligence (AI) and machine learning (ML) come in to transform the entire quality lifecycle, as we know it.
Intelligent test automation heralds the third wave in the test automation or DevOps journey, with its pre-emptive, prescriptive and predictive approach to quality. Simply put, intelligent testing uses AI and ML to address the pain points that organisations face, by introducing data-driven insights, predictions and recommendations.
It can thus automate, optimise and continuously improve the software development lifecycle. This has shifted the needle from descriptive or reactive analytics to predictive and prescriptive analytics. Some of the major challenges that organisations face today are huge amounts of test data and test results, redundant test cases, flakiness of tests and maintenance and decision-making with an overwhelming amount of information.
Intelligent testing tools can sift through high volumes of test data, analyse trends, decode patterns and forecast future trends and outcomes. The tools analyse structured and unstructured data gathered from defect management tools and automation test results and use this information to predict outcomes and suggest actionable insights.
Artificial intelligence & machine learning
The job is no longer simply to validate but to automatically detect regressions and high-risk defects in apps. Using this data-driven approach, the software can predict failures, bottlenecks, error categories and productivity struggles across the project cycles. Is this enough coverage? Are you testing more than necessary? What should you prioritise and focus on? This information is exceptionally valuable when you have a large QA backlog or are looking at the release deadline for a complex application suite. With machine learning, you can project data and make informed, proactive decisions.
These intelligent insights help in deciding the next courses of action, improving the outcomes and ultimately creating a constant feedback loop. QMetry’s Intelligent Testing tools implement solution stacks that enable agility, efficiency and quality. Our end-to-end automation led by AI and ML helps companies to:
• optimise test coverage and test depth
• increase reusability with data-driven testing
• enhance the quality of test suites with higher traceability and visibility
• weed-out duplicates and dead test cases
• predict outcomes and prescribe actionable changes
• forecast accurate and insightful decisions for release-readiness, testing adequacy and risk index
• improve the ROI while reducing time-to-market.
So, why not look into how you can leverage intelligent test automation to deliver scalable and risk-proof digital experiences faster, and with confidence?
Written by Vishal Jhala, Director/Product Development, Infostretch